* APPENDIX C 

* COVARIATE BALANCE TABLE


** OPEN STATA 14 ***


use "POLITICAL EMANCIPATION -- POLAND DATA SET.dta", clear

global covariates "jewishpop"

* 1. Change in jewish population

 *gen change_jewish1900to1921=( jewishpop-jewishpop1900)/jewishpop1900

* 2. Number public schools per capita

* 3. Literacy rates from 1931

* The 1931 census volumes include (Table 17) information on literacy at the miasto and rural level
* However, in most cases, the town level is for all towns aggregated in a given powiat.
* I have created 2 types of variables: (a) raw data from the volumes (total_over10 & literate_over10) and then (b) imputed values where
* I assign the aggregate miasto data of each powiat to all the miasta in the respective powiat (imputedtotal_over10 & imputedliterate_over10)

* 4. Electoral variables:

*turnout
* valid votes
* vote for main political parties in 1922

* For the process to add the data provided by Wittenberg (july 2020) and complted using the election returns in pdf's, see text file "steps to add Wittenberg data to my data set -- january 2024.txt"

* 5. Quality of information coded from Pinkas, etc.

* quality information in sources (recoded as 1 to 4) from variable "quality_inform" to variable "quality"

* 6. Informal "institutions": freeloan associations (per capita)



* Bandwidth: MSE // All Towns

* global covariates "jewishpop"

* jewish population change

rdrobust change_jewish1900to1921 distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all

* # public schools per capita

rdrobust schoolper10thousand distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all

* literacy rates in 1931

rdrobust prop_imputedliterate1931 distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all

* political variables

rdrobust prop_votes_1922 distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all
rdrobust prop_validvotes_1922 distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all
rdrobust prop_list1_1922 distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all
rdrobust prop_list2_1922 distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all
rdrobust prop_list3_1922 distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all
rdrobust prop_list7_1922 distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all
rdrobust prop_list8_1922 distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all
rdrobust prop_list16_1922 distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all

* quality

rdrobust quality distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all

* informal associations: freeloan per capita

rdrobust freeloan_percapita distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all

rdrobust freeloanwithmiss_percapita distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all



* ALL TOWNS & VILLAGES, bandwiths 50, 20 km

* BANDWITH: 50 km

* change in jewish population

rdrobust change_jewish1900to1921 distance, kernel(triangular) p(1) h(50000) all

* # of public schools per capita

rdrobust schoolper10thousand distance, kernel(triangular) p(1) h(50000) all

* literacy rates in 1931

rdrobust prop_imputedliterate1931 distance, kernel(triangular) p(1) h(50000) all


* political variables

rdrobust prop_votes_1922 distance, kernel(triangular) p(1) h(50000) all
rdrobust prop_validvotes_1922 distance, kernel(triangular) p(1) h(50000) all
rdrobust prop_list1_1922 distance, kernel(triangular) p(1) h(50000) all
rdrobust prop_list2_1922 distance, kernel(triangular) p(1) h(50000) all
rdrobust prop_list3_1922 distance, kernel(triangular) p(1) h(50000) all
rdrobust prop_list7_1922 distance, kernel(triangular) p(1) h(50000) all
rdrobust prop_list8_1922 distance, kernel(triangular) p(1) h(50000) all
rdrobust prop_list16_1922 distance, kernel(triangular) p(1) h(50000) all

* quality

rdrobust quality distance, kernel(triangular) p(1) h(50000) all

* informal associations: freeloan

rdrobust freeloan_percapita distance, kernel(triangular) p(1) h(50000) all

rdrobust freeloanwithmiss_percapita distance, kernel(triangular) p(1) h(50000) all

* BANDWITH: 20 km

* change in jewish population

rdrobust change_jewish1900to1921 distance, kernel(triangular) p(1) h(20000) all

* # of public schools per capita

rdrobust schoolper10thousand distance, kernel(triangular) p(1) h(20000) all

* literacy rates in 1931

rdrobust prop_imputedliterate1931 distance, kernel(triangular) p(1) h(20000) all

* political variables

rdrobust prop_votes_1922 distance, kernel(triangular) p(1) h(20000) all
rdrobust prop_validvotes_1922 distance, kernel(triangular) p(1) h(20000) all
rdrobust prop_list1_1922 distance, kernel(triangular) p(1) h(20000) all
rdrobust prop_list2_1922 distance, kernel(triangular) p(1) h(20000) all
rdrobust prop_list3_1922 distance, kernel(triangular) p(1) h(20000) all
rdrobust prop_list7_1922 distance, kernel(triangular) p(1) h(20000) all
rdrobust prop_list8_1922 distance, kernel(triangular) p(1) h(20000) all
rdrobust prop_list16_1922 distance, kernel(triangular) p(1) h(20000) all

* quality

rdrobust quality distance, kernel(triangular) p(1) h(20000) all

* informal associations: freeloan per capita

rdrobust freeloan_percapita distance, kernel(triangular) p(1) h(20000) all

rdrobust freeloanwithmiss_percapita distance, kernel(triangular) p(1) h(20000) all

* BANDWITH: 10 km

* change in jewish population

rdrobust change_jewish1900to1921 distance, kernel(triangular) p(1) h(10000) all

* # of public schools per capita

rdrobust schoolper10thousand distance, kernel(triangular) p(1) h(10000) all

* literacy rates in 1931

rdrobust prop_imputedliterate1931 distance, kernel(triangular) p(1) h(10000) all

* political variables

rdrobust prop_votes_1922 distance, kernel(triangular) p(1) h(10000) all
rdrobust prop_validvotes_1922 distance, kernel(triangular) p(1) h(10000) all
rdrobust prop_list1_1922 distance, kernel(triangular) p(1) h(10000) all
rdrobust prop_list2_1922 distance, kernel(triangular) p(1) h(10000) all
rdrobust prop_list3_1922 distance, kernel(triangular) p(1) h(10000) all
rdrobust prop_list7_1922 distance, kernel(triangular) p(1) h(10000) all
rdrobust prop_list8_1922 distance, kernel(triangular) p(1) h(10000) all
rdrobust prop_list16_1922 distance, kernel(triangular) p(1) h(10000) all

* quality

rdrobust quality distance, kernel(triangular) p(1) h(10000) all

* informal associations: freeloan per capita

*rdrobust freeloan_percapita distance, kernel(triangular) p(1) h(10000) all

rdrobust freeloanwithmiss_percapita distance, kernel(triangular) p(1) h(10000) all


*** CHANGE IN JEWISH POPULATION 1850-1900, 1850-1930, 1900-1930

. rdrobust change_jewish1850to1900 distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all
. rdrobust change_jewish1850to1930 distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all
. rdrobust change_jewish1900to1930 distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all


* Public Libraries

rdrobust public_libraries distance if type=="ville", kernel(triangular) p(1) bwselect(mserd) all

rdrobust cap_publiclibraries distance if type=="ville", kernel(triangular) p(1) bwselect(mserd) all


* Traditional Heder

rdrobust caphedertrad_any distance if pinkas==1 & type=="ville", kernel(triangular) p(1) bwselect(mserd) all


* Pogroms of 1941

rdrobust pogrom_withmissingto0 distance if type=="ville", kernel(triangular) p(1) bwselect(mserd) all


** MILITARY FRONTS

* Evacuation orders by Russian military (until early fall 1915), following information in Goldin (2022)

* Construction of variables

* variable for each moment: _may01 _june01 _july13 _august15 _sept30
* frontline==1 if any of previous variables is 1


rdrobust frontline distance if type=="ville", covs($covariates) kernel(triangular) p(1) bwselect(mserd) all

